Data Labeling in Machine Learning with Python: Explore modern ways to prepare labeled data for training and fine-tuning ML and generative AI models

Cover....1 Title Page....2 Copyright....3 Acknowledgments....4 Contributors....5 Table of Contents....8 Preface....16 Part 1: Labeling Tabular Data....22 Chapter 1: Exploring Data for Machine Learning....24 Technical requirements....25 EDA and data labeling....26 Understanding the ML project life cycle....27 Defining the business problem....27 Data discovery and data collection....27 Data exploration....28 Data labeling....28 Model training....29 Model evaluation....29 Model deployment....29 Introducing Pandas DataFrames....29 Summary statistics and data aggregates....33 Summary statistics....34 Data aggregates of the feature for each target class....35 Creating visualizations using Seaborn for univariate and bivariate analysis....36 Univariate analysis....36 Bivariate analysis....42 Profiling data using the ydata-profiling library....46 Variables section....50 Interactions section....51 Correlations....52 Missing values....54 Sample data....56 Unlocking insights from data with OpenAI and LangChain....57 Summary....65 Chapter 2: Labeling Data for Classification....68 Technical requirements....68 Predicting labels with LLMs for tabular data....69 Data labeling using Snorkel....71 What is Snorkel?....72 Why is Snorkel popular?....73 Loading unlabeled data....74 Creating the labeling functions....74 Labeling rules....74 Constants....75 Labeling functions....75 Creating a label model....77 Predicting labels....78 Labeling data using the Compose library....79 Labeling data using semi-supervised learning....80 What is semi-supervised learning?....80 What is pseudo-labeling?....81 Labeling data using K-means clustering....83 What is unsupervised learning?....83 K-means clustering....84 Inertia....86 Dunn's index....86 Summary....87 Chapter 3: Labeling Data for Regression....88 Technical requirements....89 Using summary statistics to generate housing price labels....89 Finding the closest labeled observation to match the label....90 Using semi-supervised learning to label regression data....92 Pseudo-labeling....92 Using data augmentation to label regression data....95 Using k-means clustering to label regression data....99 Summary....103 Part 2: Labeling Image Data....104 Chapter 4: Exploring Image Data....106 Technical requirements....107 Visualizing image data using Matplotlib in Python....107 Loading the data....109 Checking the dimensions....109 Visualizing the data....109 Checking for outliers....109 Performing data preprocessing....109 Checking for class imbalance....111 Identifying patterns and relationships....112 Evaluating the impact of preprocessing....112 Practice example of visualizing data....112 Practice example for adding annotations to an image....118 Practice example of image segmentation....119 Practice example for feature extraction....121 Analyzing image size and aspect ratio....123 Impact of aspect ratios on model performance....123 Image resizing....125 Image normalization....130 Performing transformations on images – image augmentation....132 Summary....135 Chapter 5: Labeling Image Data Using Rules....136 Technical requirements....136 Labeling rules based on image visualization....137 Image labeling using rules with Snorkel....137 Weak supervision....137 Rules based on the manual visualization of an image’s object color....138 Real-world applications....139 A practical example of plant disease detection....141 Labeling images using rules based on properties....143 Bounding boxes....144 Example 1 – image classification – a bicycle with and without a person....145 Example 2 – image classification – dog and cat images....147 Labeling images using transfer learning....149 Example – digit classification using a pre-trained classifier....149 Example – person image detection using the YOLO V3 pre-trained classifier....152 Example – bicycle image detection using the YOLO V3 pre-trained classifier....153 Labeling images using transformations....153 Summary....154 Chapter 6: Labeling Image Data Using Data Augmentation....156 Technical requirements....156 Training support vector machines with augmented image data....157 Kernel trick....158 Data augmentation....158 Image data augmentation....158 Implementing an SVM with data augmentation in Python....160 Introducing the CIFAR-10 dataset....160 Loading the CIFAR-10 dataset in Python....160 Preprocessing the data for SVM training....161 Implementing an SVM with the default hyperparameters....162 Evaluating SVM on the original dataset....163 Implementing an SVM with an augmented dataset....163 Training the SVM on augmented data....164 Evaluating the SVM’s performance on the augmented dataset....164 Image classification using the SVM with data augmentation on the MNIST dataset....165 Convolutional neural networks using augmented image data....167 How CNNs work....167 Practical example of a CNN using data augmentation....169 CNN using image data augmentation with the CIFAR-10 dataset....174 Summary....177 Part 3: Labeling Text, Audio, and Video Data....180 Chapter 7: Labeling Text Data....182 Technical requirements....182 Real-world applications of text data labeling....183 Tools and frameworks for text data labeling....185 Exploratory data analysis of text....187 Loading the data....187 Understanding the data....187 Cleaning and preprocessing the data....187 Exploring the text’s content....188 Analyzing relationships between text and other variables....188 Visualizing the results....188 Exploratory data analysis of sample text data set....188 Exploring Generative AI and OpenAI for labeling text data....192 GPT models by OpenAI....193 Zero-shot learning capabilities....193 Text classification with OpenAI models....193 Data labeling assistance....193 OpenAI API overview....193 Use case 1 – summarizing the text....194 Use case 2 – topic generation for news articles....197 Use case 3 – classification of customer queries using the user-defined categories and sub-categories....197 Use case 4 – information retrieval using entity extraction....200 Use case 5 – aspect-based sentiment analysis....202 Hands-on labeling of text data using the Snorkel API....203 Hands-on text labeling using Logistic Regression....210 Hands-on label prediction using K-means clustering....213 Generating labels for customer reviews (sentiment analysis)....215 Summary....218 Chapter 8: Exploring Video Data....220 Technical requirements....221 Loading video data using cv2....221 Extracting frames from video data for analysis....222 Extracting features from video frames....223 Color histogram....223 Optical flow features....225 Motion vectors....225 Deep learning features....226 Appearance and shape descriptors....226 Visualizing video data using Matplotlib....227 Frame visualization....228 Temporal visualization....228 Motion visualization....229 Labeling video data using k-means clustering....230 Overview of data labeling using k-means clustering....231 Example of video data labeling using k-means clustering with a color histogram....231 Advanced concepts in video data analysis....235 Motion analysis in videos....235 Object tracking in videos....237 Facial recognition in videos....239 Video compression techniques....240 Real-time video processing....241 Video data formats and quality in machine learning....243 Common issues in handling video data for ML models....244 Troubleshooting steps....244 Summary....245 Chapter 9: Labeling Video Data....246 Technical requirements....247 Capturing real-time video....247 Key components and features....248 A hands-on example to capture real-time video using a webcam....248 Building a CNN model for labeling video data....249 Using autoencoders for video data labeling....255 A hands-on example to label video data using autoencoders....257 Transfer learning....264 Using the Watershed algorithm for video data labeling....266 A hands-on example to label video data segmentation using the Watershed algorithm....266 Computational complexity....271 Performance metrics....271 Real-world examples for video data labeling....272 Advances in video data labeling and classification....273 Summary....275 Chapter 10: Exploring Audio Data....276 Technical requirements....277 Real-life applications for labeling audio data....277 Audio data fundamentals....280 Hands-on with analyzing audio data....283 Example code for loading and analyzing sample audio file....283 Best practices for audio format conversion....286 Example code for audio data cleaning....287 Extracting properties from audio data....289 Tempo....289 Chroma features....290 Mel-frequency cepstral coefficients (MFCCs)....291 Zero-crossing rate....292 Spectral contrast....293 Considerations for extracting properties....294 Visualizing audio data with matplotlib and Librosa....294 Waveform visualization....295 Loudness visualization....296 Spectrogram visualization....297 Mel spectrogram visualization....298 Considerations for visualizations....301 Ethical implications of audio data....301 Recent advances in audio data analysis....303 Troubleshooting common issues during data analysis....304 Troubleshooting common installation issues for audio libraries....306 Summary....308 Chapter 11: Labeling Audio Data....310 Technical requirements....311 Downloading FFmpeg....312 Azure Machine Learning....312 Real-time voice classification with Random Forest....312 Transcribing audio using the OpenAI Whisper model....317 Step 1 – importing the Whisper model....318 Step 2 – loading the base Whisper model....318 Step 3 – setting up FFmpeg....319 Step 4 – transcribing the YouTube audio using the Whisper model....320 Classifying a transcription using Hugging Face transformers....321 Hands-on – labeling audio data using a CNN....321 Exploring audio data augmentation....328 Introducing Azure Cognitive Services – the speech service....333 Creating an Azure Speech service....333 Speech to text....334 Speech translation....335 Summary....337 Chapter 12: Hands-On Exploring Data Labeling Tools....338 Technical requirements....339 Azure Machine Learning data labeling....339 Label Studio....339 pyOpenAnnotate....339 Data labeling using Azure Machine Learning....339 Benefits of data labeling with Azure Machine Learning....340 Data labeling steps using Azure Machine Learning....340 Image data labeling with Azure Machine Learning....341 Text data labeling with Azure Machine Learning....355 Audio data labeling using Azure Machine Learning....363 Integration of the Azure Machine Learning pipeline with the labeled dataset....366 Exploring Label Studio....367 Labeling the image data....368 Labeling the text data....372 Labeling the video data....373 pyOpenAnnotate....374 Computer Vision Annotation Tool....376 Comparison of data labeling tools....377 Advanced methods in data labeling....378 Active learning....379 Semi-automated labeling....379 Summary....380 Index....382 About PACKT....394 Other Books You May Enjoy....395